{
 "metadata": {
  "signature": "sha256:6a0d87c0286ee4da39c6ff0e5eee67a824a84cefe7a7f6023b9ceef3114d20bb"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "id": "9A756AD9B8CA40BE8234756F049F83D5",
     "metadata": {},
     "source": [
      "```\n",
      "\u91cd\u8981\u8bf4\u660e\uff1a\n",
      "\u672c\u77e5\u8bc6\u5e93\u6587\u6863\uff0c\u5305\u542b\u5f53\u524d\u7248\u672c\u4e0d\u652f\u6301\u7684\u529f\u80fd\uff0c\u4ec5\u4f5c\u4e3a\u9759\u6001\u5c55\u793a\uff0c\u4e0d\u652f\u6301\u5728 Notebook \u73af\u5883\u8fd0\u884c\u3002\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "id": "F4B335E8553D4FA18E3D5B7FD4582F4E",
     "metadata": {},
     "source": [
      "## \u9ad8\u4f4e\u70b9\u7a81\u7834\n",
      "\u5229\u7528\u4ef7\u683c\u5bf9\u67d0\u52a8\u6001\u9ad8\u70b9\u6216\u4f4e\u70b9\u7684\u7a81\u7834\u4ea7\u751f\u4ea4\u6613\u4fe1\u53f7, \u4ee5\u5cf0\u503c\u9ad8\u4f4e\u70b9\u7a81\u7834\u7b56\u7565\u4e3a\u4f8b\n",
      "### 1 \u5cf0\u503c\u9ad8\u4f4e\u70b9\u5b9a\u4e49\n",
      "* \u5cf0\u503c\u9ad8\u70b9\uff1a\u4e00\u6bb5\u65f6\u95f4\u5e8f\u5217\u7684\u6b63\u4e2d\u95f4\u4e3a\u6700\u9ad8\u503c\uff0c\u8be5\u6700\u9ad8\u503c\u4e3a\u6709\u6548\u7684\u5cf0\u503c\u9ad8\u70b9\uff1b\n",
      "* \u5cf0\u503c\u4f4e\u70b9\uff1a\u4e00\u6bb5\u65f6\u95f4\u5e8f\u5217\u7684\u6b63\u4e2d\u95f4\u4e3a\u6700\u4f4e\u503c\uff0c\u8be5\u6700\u9ad8\u503c\u4e3a\u6709\u6548\u7684\u5cf0\u503c\u4f4e\u70b9\u3002"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "557EE43EE04747A5B9EB3FA5A01558E2",
     "input": [
      "from __future__ import division \n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt \n",
      "import matplotlib.dates as mdate\n",
      "from matplotlib.font_manager import FontProperties\n",
      "from matplotlib.ticker import MultipleLocator,FormatStrFormatter \n",
      "from CAL.PyCAL import * \n",
      "cal = Calendar('China.SSE')\n",
      "\n",
      "def box_plot(boxes, **args):\n",
      "    '''\n",
      "    Display self-definited box plot.\n",
      "    \n",
      "    Return a matplotlib figure exhibiting box plot of appointed boxes. \n",
      "    \n",
      "    Parameters\n",
      "    -------\n",
      "    boxes: dict-like\n",
      "      {\n",
      "          'boxes': array of instance of array-like.\n",
      "          'color': array of basestring instance.\n",
      "      }\n",
      "    \n",
      "    xlim: tuple-like, optional\n",
      "      Set the limitations of x-axis.\n",
      "    ylim: tuple-like, optional\n",
      "      Set the limitations of y-axis.\n",
      "    title: string, optional\n",
      "      Set the title of the plot.\n",
      "    xlabel: string, optional\n",
      "      Set the label of x-axis.\n",
      "    ylabel: string, optional\n",
      "      Set the label of y-axis.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    fig: matplotlib.figure.Figure \n",
      "      Figure instance revelant on the portfolio value and benchmark\n",
      "    \n",
      "    '''\n",
      "    fontsize, titlesize, labelsize, legendsize, linewidth = 20, 20, 16, 12, 5\n",
      "    titleFont = font.copy()\n",
      "    titleFont.set_size(titlesize)\n",
      "    labelFont = font.copy()\n",
      "    labelFont.set_size(labelsize)\n",
      "    legendFont = font.copy()\n",
      "    legendFont.set_size(legendsize)    \n",
      "    \n",
      "    fig = plt.figure(figsize = (12,6))\n",
      "    ax = fig.add_subplot(111)\n",
      "    box_plot = ax.boxplot(boxes.get('boxes'), widths = 0.2, patch_artist=True)\n",
      "    \n",
      "    plt.grid(True, axis = 'y')\n",
      "    title = args.get('title') if args.get('title') else u'\u7bb1\u56fe\u793a\u4f8b'\n",
      "    xlabel = args.get('xlabel') if args.get('xlabel') else u'x\u8f74'\n",
      "    ylabel = args.get('ylabel') if args.get('ylabel') else u'y\u8f74'\n",
      "    plt.title(title,fontsize = fontsize,verticalalignment = 'bottom',horizontalalignment = 'center',fontproperties = titleFont)\n",
      "    plt.xlabel(xlabel,fontsize=fontsize,verticalalignment = 'top', horizontalalignment = 'center',fontproperties = labelFont)\n",
      "    plt.ylabel(ylabel, fontsize = fontsize, verticalalignment = 'bottom', horizontalalignment = 'right',rotation=0, fontproperties = labelFont)\n",
      "    if args.get('xlim'):\n",
      "        plt.xlim(args.get('xlim')[0], args.get('xlim')[1])\n",
      "    if args.get('ylim'):\n",
      "        plt.ylim(args.get('ylim')[0], args.get('ylim')[1])    \n",
      "    for index,box in enumerate(box_plot['boxes']):\n",
      "        box.set(facecolor = boxes.get('colors')[index])\n",
      "        box_plot['medians'][index].set(color = boxes.get('colors')[index])\n",
      "    return fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "0091BFC9B17E40299AA91C25B6F53080",
     "input": [
      "boxes = {\n",
      "    'boxes':[[100,110,120,130],[110,120,130,140],[120,130,140,150],\n",
      "    [110,120,130,140],[100,110,120,130]],\n",
      "    'colors':['r']*3 + ['g']*2\n",
      "}\n",
      "box_plot(boxes, xlim = (0,6),ylim = (80,160), title = u'\u5cf0\u503c\u9ad8\u70b9\u793a\u4f8b\u56fe',xlabel = u'\u65f6\u95f4', ylabel = u'\u70b9\u6570')\n",
      "plt.annotate(u'\u5cf0\u503c\u9ad8\u70b9', xy=(3, 150),xycoords='data', xytext=(-90, 10),textcoords='offset points', fontsize=16,arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=-0.8\"),fontproperties = font.copy())\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "data": {
        "image/png": 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OenGunCs5dOhQjRkzRtKlK+knTvzvseGpqakKCAgos7+GDT1lsbiXuR/Mq7QH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\n",
        "text/plain": "<matplotlib.figure.Figure at 0x8e42ad0>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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OenGunCs5dOhQjRkzRtKlK+knTvzvseGpqakKCAgos7+GDT1lsbiXuR/Mq7QH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\n",
       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0x8e42ad0>"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "CF8EECAEFCEC4B2FA831E2C098DF4960",
     "input": [
      "boxes = {\n",
      "    'boxes':[[110,120,130,140], [100,110,120,130], [90,100,110,120], \n",
      "    [100,110,120,130],[110,120,130,140]],\n",
      "    'colors':['g']*3 + ['r']*2\n",
      "}\n",
      "box_plot(boxes, xlim = (0,6),ylim = (80,150), title = u'\u5cf0\u503c\u4f4e\u70b9\u793a\u4f8b\u56fe',xlabel = u'\u65f6\u95f4', ylabel = u'\u70b9\u6570')\n",
      "plt.annotate(u'\u5cf0\u503c\u4f4e\u70b9', xy=(3, 95),xycoords='data', xytext=(-90, -50),textcoords='offset points', fontsize=16,arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.5\"),fontproperties = font.copy())\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "data": {
        "image/png": 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tvoX2B/3kLPzudF30XVVR1Jcnl70M5uqbRD///HMtWrRI8+fPV/Xq1e3toaGh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\nWadOnZKHh4eGDBmiBx54wNlloYSysrJ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        "text/plain": "<matplotlib.figure.Figure at 0x8ec2550>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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tvoX2B/3kLPzudF30XVVR1Jcnl70M5uqbRD///HMtWrRI8+fPV/Xq1e3toaGh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\nRK5CYII=\n",
       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0x8ec2550>"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "70E19033338A43BF858CDFFDD15EA397",
     "metadata": {},
     "source": [
      "### 2 \u5cf0\u503c\u9ad8\u4f4e\u70b9\u7a81\u7834 - \u65e0\u6b62\u76c8\u6b62\u635f\n",
      "* \u524d\u6536\u76d8\u4ef7\u683c $pre_{closeprice}$ \u5411\u4e0a\u7a81\u7834\u52a8\u6001\u5cf0\u503c\u9ad8\u70b9\uff0c\u5f62\u6210\u505a\u591a\u4fe1\u53f7\uff0c\u4e70\u5165\u5f00\u4ed3\uff1b\n",
      "* \u524d\u6536\u76d8\u4ef7\u683c $pre_{closeprice}$ \u5411\u4e0b\u7a81\u7834\u52a8\u6001\u5cf0\u503c\u4f4e\u70b9\uff0c\u5f62\u6210\u505a\u7a7a\u4fe1\u53f7\uff0c\u5356\u51fa\u5f00\u4ed3\u3002"
     ]
    },
    {
     "cell_type": "strategy",
     "collapsed": false,
     "has_detail": true,
     "id": "3D1E38F1D0BA412983C445F92796CCC7",
     "input": "#**********************************************#\n# \u5cf0\u503c\u9ad8\u4f4e\u70b9\u7a81\u7834 - \u65e0\u6b62\u76c8\u6b62\u635f\n# \u4ef7\u683c\u4e0a\u7a7f\u5cf0\u503c\u9ad8\u70b9\uff0c\u5f62\u6210\u505a\u591a\u4fe1\u53f7\uff0c\u4e70\u5165\u5f00\u4ed3\uff1b\n# \u4ef7\u683c\u4e0b\u7a7f\u5cf0\u503c\u9ad8\u70b9\uff0c\u5f62\u6210\u505a\u7a7a\u4fe1\u53f7\uff0c\u5356\u51fa\u5f00\u4ed3\uff1b\n#**********************************************#\nimport talib\nimport pandas as pd\nimport numpy as np\nimport quartz_futures as qf\nfrom quartz_futures.api import *\n\n### \u53c2\u6570\u521d\u59cb\u5316\nuniverse = ['RB1610']               # \u7b56\u7565\u8bc1\u5238\u6c60\nstart = pd.datetime(2016, 6, 1)   # \u56de\u6d4b\u5f00\u59cb\u65f6\u95f4\nend   = pd.datetime(2016, 9, 1)   # \u56de\u6d4b\u7ed3\u675f\u65f6\u95f4\ncapital_base = 1e4                  # \u521d\u8bd5\u53ef\u7528\u8d44\u91d1\nrefresh_rate = 1                  # \u8c03\u4ed3\u5468\u671f\nfreq = 'd'                          # \u8c03\u4ed3\u9891\u7387\uff1as -> \u79d2\uff1bm-> \u5206\u949f\uff1bd-> \u65e5\uff1b\n\n## \u81ea\u52a8\u751f\u6210\u4fdd\u8bc1\u91d1\u6bd4\u4f8b\uff1a margin_rate\nmargin_ratio = DataAPI.FutuGet(ticker = universe, field = ['ticker','tradeMarginRatio'], pandas = '1')\nmargin_rate = dict(zip(margin_ratio.ticker.tolist(), [0.01*index for index in margin_ratio.tradeMarginRatio.tolist()]))\naccounts = {\n    'futures_account': AccountConfig(account_type='futures', \n                                     capital_base=capital_base, margin_rate=margin_rate)\n}\n\n\n\n### \u7b56\u7565\u521d\u59cb\u5316\u51fd\u6570\uff0c\u4e00\u822c\u7528\u4e8e\u8bbe\u7f6e\u8ba1\u6570\u5668\uff0c\u56de\u6d4b\u8f85\u52a9\u53d8\u91cf\u7b49\u3002\ndef initialize(context):\n    context.high_point = None\n    context.low_point = None\n    pass\n\n### \u56de\u6d4b\u8c03\u4ed3\u903b\u8f91\uff0c\u6bcf\u4e2a\u8c03\u4ed3\u5468\u671f\u8fd0\u884c\u4e00\u6b21\uff0c\u53ef\u5728\u6b64\u51fd\u6570\u5185\u5b9e\u73b0\u4fe1\u53f7\u751f\u4ea7\uff0c\u751f\u6210\u8c03\u4ed3\u6307\u4ee4\u3002\ndef handle_data(context):\n    futures_account = context.get_account('futures_account')\n    symbol, amount = universe[0], 1\n    history_data = context.history(symbol = symbol, attribute=['highPrice', 'lowPrice', 'closePrice'], time_range = 5)[symbol]\n    pre_close_price = history_data['closePrice'].tolist()[-1]\n    if history_data.highPrice.tolist().index(history_data.highPrice.max()) == int(len(history_data)/2):\n        context.high_point = history_data.highPrice.max()\n    if history_data.lowPrice.tolist().index(history_data.lowPrice.min()) == int(len(history_data)/2):\n        context.low_point = history_data.highPrice.min()\n    \n    current_long = futures_account.get_positions().get(symbol, dict()).get('long_amount', 0)\n    current_short = futures_account.get_positions().get(symbol, dict()).get('short_amount', 0)    \n    if pre_close_price > context.high_point:\n        if current_short > 0:\n            print context.current_date, '\u4e70\u5165\u5e73\u4ed3'\n            futures_account.order(symbol, current_short, 'close')\n        if current_long < amount:\n            print context.current_date, '\u4e70\u5165\u5f00\u4ed3'\n            futures_account.order(symbol, amount, 'open')\n            \n    if pre_close_price < context.low_point:\n        if current_long > 0:\n            print context.current_date, '\u5356\u51fa\u5e73\u4ed3'\n            futures_account.order(symbol, -current_long, 'close')\n        if current_short < amount:\n            print context.current_date, '\u5356\u51fa\u5f00\u4ed3'\n            futures_account.order(symbol, -amount, 'open')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "2016-06-01 00:00:00"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \u4e70\u5165\u5f00\u4ed3\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "2016-06-02 00:00:00 \u5356\u51fa\u5e73\u4ed3\n2016-06-02 00:00:00 \u5356\u51fa\u5f00\u4ed3\n2016-06-07 00:00:00 \u4e70\u5165\u5e73\u4ed3\n2016-06-07 00:00:00 \u4e70\u5165\u5f00\u4ed3\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "2016-07-25 00:00:00 \u5356\u51fa\u5e73\u4ed3\n2016-07-25 00:00:00 \u5356\u51fa\u5f00\u4ed3\n2016-07-29 00:00:00 \u4e70\u5165\u5e73\u4ed3\n2016-07-29 00:00:00 \u4e70\u5165\u5f00\u4ed3\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "2016-08-05 00:00:00 \u5356\u51fa\u5e73\u4ed3\n2016-08-05 00:00:00 \u5356\u51fa\u5f00\u4ed3\n2016-08-08 00:00:00 \u4e70\u5165\u5e73\u4ed3\n2016-08-08 00:00:00 \u4e70\u5165\u5f00\u4ed3\n2016-08-19 00:00:00 \u5356\u51fa\u5e73\u4ed3\n2016-08-19 00:00:00 \u5356\u51fa\u5f00\u4ed3\n"
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